Utkarsh Kamthankar

Results-driven Data Analyst skilled in data engineering, machine learning, and business intelligence. Expertise in building optimized data pipelines, developing dashboards, and implementing predictive models to drive business decisions. Proficient in Python, Power BI, and SQL, with a strong ability to transform data into actionable insights. Currently pursuing an MSc in Data Science, focusing on advanced analytics.

• Microsoft Power BI Data Analyst Professional Certificate
• Google Project Management: Professional Certificate
• Microsoft Business Analyst Professional Certificate
• Machine Learning Specialization

1. End-to-End Data Engineering Pipeline for E-Commerce Analytics. 

• Built a complete data pipeline for automating data ingestion, transformation, and storage for an e-commerce platform.
• Apache Spark, SQL, AWS S3, Implemented data quality checks and scheduled ETL processes using Airflow, leading to a 30% reduction in data errors and faster reporting times.
• Deployed Power BI dashboards for real-time analytics on customer behavior and sales trends.

2. RAG-Based Chatbot for Academic Research 

• Developed a chatbot using Retrieval-Augmented Generation (RAG) to assist researchers in interacting with academic materials.
• Integrated LLMs to generate contextual responses and summaries, enhancing research efficiency.
• Deployed the chatbot as a web application, enabling real-time access to academic resources.

3. Sales and Inventory Management Analytics

• Built a comprehensive sales and inventory management system for a retail company using SQL to query data from multiple sources and integrate it into a Power BI dashboard.
• Analyzed key performance indicators (KPIs) such as stock turnover rates, sales trends, and supply chain efficiency, resulting in a 20% increase in operational efficiency.
• Created automated reports for management to track sales performance, product demand, and supplier lead times in real-time.

4. Customer Churn Prediction Model

• Developed a machine learning model to predict customer churn for a telecom company using Python, scikit-learn, and XGBoost.
• Performed feature engineering and data preprocessing on customer behavior data stored in SQL databases.
• Achieved an AUC score of 0.85 on the test data, enabling the marketing team to target at-risk customers with retention campaigns, reducing churn by 15%.